1. A community-driven annotation platform for structural genomics Workshop on the Biological Annotation of Novel Proteins, March 7-8, 2008 Biomedical theme: Central Machinery of Life -proteins conserved in all kingdoms of life Biological theme: Complete coverage of Thermotoga maritima Adam Godzik and the JCSG Bioinformatics Team

2. Science is all about communication Since late XIX century, a dominant way of communicating scientific results is through peer-reviewed manuscripts Pro –Peer review ensures quality –Enforces a “publishable unit” – decreases noise in the “communication space” –Authorship rules ensure proper distribution of credit in a system that is well integrated with system of promotions and evaluations Con –Significant time lag and additional costs –Enforces a “publishable unit” – below the threshold results are lost –Not scalable with high throughput data production

3. Increasingly, it’s not the only game in town Databases and automated annotation protocols –pro: fast, machine searchable, scalable –con: difficult to ensure quality and assign credit, put the burden of expertise on the user Wikipedia –pro: harnesses power of community, scalable –con: unreliable, difficult to ensure quality and assign credit

4. Can we have the best of all worlds? Peer-reviewed manuscript Automated database annotation Wikipedia entry Fast, accurate, scalable

5. purificationexpression cloning struc. refinement struc. validation annotation publication phasing data collection xtal screening tracing xtal mounting crystallizationimaging harvesting target selection PDB Structure determination in PSI centers is done on a semi-automated assembly line Joint Center for Structural Genomics One of four large scale (production) centers of PSI2 ~ 600 structures deposited in the PDB Sustained pace of ~15 PDB depositions per month

6. …and the pace of structure determination far outstrips the pace of our publications…  PSI-1 PSI-2 Structure collage: PSI Publication statistics : http://olenka.med.virginia.edu/psi/

7. Why? Speed: 2-3 months from target selection to structure, 2-3 structures per week in each center Assembly line process, no time to develop “special relationship” with each protein Structures are not associated with ongoing biochemical and biological research. Targets selected based on novelty, no expertise available anywhere, difficult to reach “publishable unit”

8. We are not alone … Bacterial genomes –1995-2000 - every new sequenced genome led to a Nature/Science publication –with 500+ genomes, an increasing percentage of them never become a single focus of a specific publication –Community based annotation efforts become the best source of information (SEED)

9. WEB 2.0 is reshaping how we share information: Communities of globally distributed peers (Networks) built around rich, collaborative environments. Wikipedia Citizendium Scholarpedia Google Knols OpenWetware Wikiomics WikiProteins TOPSAN

10. How can we tap into an ultimate research tool? Search engines are becoming serious research tools Google indexes research papers, books, wikipedia pages Semi-natural language searches

11. Structural coverage of many genomes (here T.maritima) approaches completeness ~ 73% of feasible targets

12. http://research.calit2.net/metagenomics/thermotoga Which brings attention from broader communities

13. We can utilize other information Metabolic reconstruction of T.maritima was done in collaboration with UCSD Systems Biology Lab (Bernard Palsson) Model is consistent with all the published experimental data on TM (see Ines Thiele poster) First generation model covers 479 genes (1398 are not in the model), 492 metabolites –Proteins coded by 113 of these genes have been solved (71 at JCSG, 28 at other PSI centers) –320 have be modeled –We know at least approximate structure of ALL the proteins in the reconstruction

14. And bring it together to help make sense of the structures and see them in the full context

15. All available information about a protein on one page

17. We try to combine automated, database driven annotations with expert curated input. Annotation: Feeds from public databases Expert-curated information Content management: Wiki-style editing (WYSIWYG editor) Page-level access control Structured fields + free text Instant publication Always open for comments and edits Quality control & authorship: Encourage community collaboration JCSG scientists & invited peers Many authors - no contribution too small Lead authors (editors) in charge of releases

18. We tried it before Internal structure annotation system developed at JCSG in 2003 Led to several interesting discussions, but mostly didn’t catch on

19. TOPSAN content:

20. Protein Groups

21. Browsing Options

22. JCSG: Structures / Structure Notes / TOPSAN 278 of 593 structures have an annotation on TOPSAN

23. Members of the biological community can utilize PSI structures only when they are aware of them Functionally well characterized enzyme and is also a new fold. PDB ID: 3C8W TargetDB: 376561

24. TOPSAN access statistics- Jan to Mar 08 1143 visits from rest of the world.

25. Google sent us the largest number of visitors.

26. UCSD & Burnham Bioinformatics Core John Wooley Adam Godzik Lukasz Jaroszewski Slawomir Grzechnik Sri Krishna Subramanian Andrew Morse Tamara Astakhova Lian Duan Piotr Kozbial Dana Weekes Natasha Sefcovic Josie Alaoen GNF & TSRI Crystallomics Core Scott Lesley Mark Knuth Heath Klock Marc Deller Dennis Carlton Polat Abdubek Sanjay Agarwalla Connie Chen Thomas Clayton Dustin Ernst Julie Feuerhelm Regina Gorski Anna Grzechnik Joanna C. Hale Thamara Janaratne Hope Johnson Sachin Kale Daniel McMullan Edward Nigoghossian Amanda Nopakun Linda Okach Jessica Paulsen Christina Puckett Sebastian Sudek Jessica Canseco Scientific Advisory Board Sir Tom Blundell Univ. Cambridge Homme Hellinga Duke University Medical Center James Naismith The Scottish Structural Proteomics facility Univ. St. Andrews James Paulson Consortium for Functional Glycomics, The Scripps Research Institute Robert Stroud Center for Structure of Membrane Proteins, Membrane Protein Expression Center UC San Francisco Soichi Wakatsuki Photon Factory, KEK, Japan James Wells UC San Francisco Todd Yeates UCLA-DOE, Inst. for Genomics and Proteomics TSRI NMR Core Kurt Wüthrich Reto Horst Margaret Johnson Amaranth Chatterjee Michael Geralt Wojtek Augustyniak Jin-Kyu Rhee Biswaranjan Mohanty Bill Pedrini Pedro Serrano TSRI Administrative Core Ian Wilson Marc Elsliger Gye Won Han David Marciano Henry Tien Xiaoping Dai Lisa van Veen Stanford /SSRL Structure Determination Core Keith Hodgson Ashley Deacon Mitchell Miller Hsiu-Ju (Jessica) Chiu Debanu Das Kevin Jin Abhinav Kumar Winnie Lam Silvya Oommachen Christopher Rife Scott Talafuse Christine Trame Qingping Xu Henry van den Bedem Ronald Reyes The JCSG is supported by the NIH Protein Structure Initiative grant U54 GM074898 from the National Institute of General Medical Sciences (www.nigms.nih.gov).

27. Thermotoga browser acknowledgments Co-PI of the project - Andrei Osterman (the biochemistry side, specific examples) The JCSG team - for all the structures, focus on Thermotoga and CML Bernard Palsson group and Ines Thiele for work with Thermotoga reconstruction and model simulations The JCMM team for structure modeling Krzysztof Ginalski and bioinfor server team for assistance with “borderline” predictions Ying Zhang (JCMM) - finalizing the metabolic reconstruction, network and fold distribution analysis Dana Weekes (JCSG) - first pass on the Thermotoga metabolic reconstruction, TM TOPSAN pages Craig Shepherd (JCMM) - network visualization Zhanwen Li (JCMM) - modeling and fold assignments